{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/matt\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "home_folder = os.path.expanduser(\"~\")\n",
    "print(home_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/matt/Data/Ionosphere/ionosphere.data\n"
     ]
    }
   ],
   "source": [
    "# Change this to the location of your dataset\n",
    "data_folder = os.path.join(home_folder, \"Data\", \"Ionosphere\")\n",
    "data_filename = os.path.join(data_folder, \"ionosphere.data\")\n",
    "print(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "# Size taken from the dataset and is known\n",
    "X = np.zeros((351, 34), dtype='float')\n",
    "y = np.zeros((351,), dtype='bool')\n",
    "\n",
    "with open(data_filename, 'r') as input_file:\n",
    "    reader = csv.reader(input_file)\n",
    "    for i, row in enumerate(reader):\n",
    "        # Get the data, converting each item to a float\n",
    "        data = [float(datum) for datum in row[:-1]]\n",
    "        # Set the appropriate row in our dataset\n",
    "        X[i] = data\n",
    "        # 1 if the class is 'g', 0 otherwise\n",
    "        y[i] = row[-1] == 'g'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 263 samples in the training dataset\n",
      "There are 88 samples in the testing dataset\n",
      "Each sample has 34 features\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)\n",
    "print(\"There are {} samples in the training dataset\".format(X_train.shape[0]))\n",
    "print(\"There are {} samples in the testing dataset\".format(X_test.shape[0]))\n",
    "print(\"Each sample has {} features\".format(X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "estimator = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 86.4%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = estimator.predict(X_test)\n",
    "accuracy = np.mean(y_test == y_predicted) * 100\n",
    "print(\"The accuracy is {0:.1f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy is 82.3%\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "average_accuracy = np.mean(scores) * 100\n",
    "print(\"The average accuracy is {0:.1f}%\".format(average_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_scores = []\n",
    "all_scores = []\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "    avg_scores.append(np.mean(scores))\n",
    "    all_scores.append(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f272a9592e8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ZAFZ3H2EsmTJFevZZMz/+eOnFF6WM\nDO9rSgTDh7vf+Jk2zRmtCf8tXCht3x6eWZaUm+tPPdFGgxKRtGGDVFUVngWDUrdu0XtNxrzCC7Nn\nP6MVKy5T85qTNTppxYpReuiheRE4CwAAAAAQ62hQAvBVPI15/fvfpV//2szT06Xnn5e6d/e8pITi\ndotyyxbpqae8rwWmggIzGz1aOuUU72vxgluDct067+tAYigsNLMzzoju7tZhw8xs2bLovR6ST2Vl\npWbOLGzSWNf6VFSM0AMPvKfKuvPEAQAAAAAJhwYlAF/FS4Ny5UpnlKWbxx6TBg/2tp5ElJMjXXih\nmd9zD7co/bZpk7RkiZnn5Xlfi1f69JHS0sKzL76Q9uzxpx7ENy/3T9Zwa1B++KFUUhLd10XymDPn\nWRUXj4v4ucXF4/Toowsifi4AAAAAILbQoATgK7cG5bp1Unm597XUZ9s26ZprzPF8knT33dKNN3pf\nU6Jyu0W5dav0xBPe14JvuO2ezMyURozwvhavpKVJffuaOWNe0RR+NCiPP17q18/MuUWJSJk/v0ih\nkMus/mYKhbI1fz5vtgAAAACQ6GhQAvBVt27SySeHZ6GQ9M47/tRTV1mZdMUV7jdOxo6Vpk71vKSE\nNnSodPHFZn7PPdKhQ97XA2nfPukvfzHzSZOklAT/rwj2UCISQiFp7Vozz86O/mu77aGkQYlICIVC\n2rWrZdTO37mzpaqrq6N2PgAAAADAfwn+0SKAeDBkiJnFwpjXw4elG26Q1q83HzvnHOnPf47u/rBk\n5XaL8uOPpccf974WOP/e9+0LzwIB6eab/anHSzQoEQlbt0oVFeFZu3ZSjx7Rf223Ma9Ll0b/dZH4\ntm7dqpKSXlE7v6QkU8XFxVE7HwAAAADgPxqUAHwXq3sof/Yz6eWXzfzEE6V//lNq1cr7mpLB4MHS\npZea+b33covSa7YtFRSY+Xe/K7Vv7309XnNrUK5b530diG9u412zs735BpecHDPbuFHavTv6r43E\nVlJSorKyzlE7v6yss0pYmAoAAAAACY0GJQDfuTUoV692xuL5ZfZs6YEHzDwQkF58Ueocvc/kIPdb\nlJ984j5qFNGzdKnTzKhr0iTva/FDVpaZbdhAoxyN48f+yRodO7o32hnzCgAAAAAA/EaDEoDvBgyQ\n2rYNz8rK3EeremHxYmnyZDO3LOmpp9ybFoisc86RLrvMzH/9a6mqyvt6ktWsWWY2bJjUv7/npfii\nUyfp+OPDs0OHpM2b/akH8am+G5RecRvzSoMSzRUMBpWREb2ruBkZuxUMBqN2PgAAAADAfzQoAfgu\nNVUaNMjM/RjzunGjNGaM++3NGTOkK67wvqZk5XaLcvt26bHHvK8lGW3fLj3/vJkny+3JGuyhRHPY\ntlRYaOZe3aCUpOHDzYw9lGiunj17KhjcErXzg8FiZWZmRu18AAAAAID/aFACiAluY15XrfK2hi+/\nlEaPlr7+2nxs/Hjppz/1tp5kd9ZZ7g3h3/xGqqz0vp5k8/DDUnV1eNatm3TVVf7U4xcalGiOTz+V\n/v3v8KxVK6l3b+9quOACc9/lli3Szp3e1YDEk5qaqi5dojfSoGvXKqWk8D9VAQAAACCR8b/6AMSE\nIUPMzMsblJWV0ne+I338sfnYsGHOqMu6H/Ai+txuUX72mTR3rve1JJODB6VHHjHziROltDTv6/GT\n20jndeu8rwPxyW2868CBzuQAr3To4P77mDGvaK4xYwYoNXVtxM9NTS3UmDEu3x0CAAAAAEgoNCgB\nxIRBg6S63yi/bZu0a1f0X9u2pVtvld54w3wsM1NasEBq2TL6dcCUnS1dfbWZ/+Y3ThMN0TF/vnOj\nuLa0NGnCBH/q8RM3KNEcfu+frMGYV0TDhAnXKTPzqYifm5n5lMaPvzbi5wIAAAAAYgsNSgAxoW1b\n90aAF7cop0+X/vpXMz/2WGnhQuf2CfwzdaqZ7dwpzZnjeSlJY9YsM7v+eqlTJ+9r8dtpp5m33Xbu\nNBu4gBu3BqWX+ydr0KBENKSnp2vy5GwFAq9H7MxAYIluu+1MpaenR+xMAAAAAEBsokEJIGa47aGM\ndoPyueekX/zCzFu0kJ59VurVK7qvj6PLypKudblI8dvfSgcOeF9PoluzRnr7bTPPy/O+lliQni71\n6WPmRUXe14L4U1hoZn40KM8/35xS8NFH0o4d3teCxJKbO1YXXPCSpD0ROG2PcnJe0cSJ10fgLAAA\nAABArKNBCSBmuDUoV62K3uu9/750003OiNe6HnxQuvDC6L02GmfKFDPbvVt6+GHva0l0brcnzzhD\nOvdc72uJFW63u9lDiaPZtct5n6otLU3q18/7Wtq3dx8tyx5KNJdlWZo3b5oGD56m5jUp92jw4Hs0\nb940WSz9BgAAAICkQIMSQMwYMsTM3n9f2r8/8q+1c6d0xRXuZ//0p8m5ay+WDRjgjBit6777ovP7\nI1mVlEhPP23meXlSMn9enJVlZuyhxNG4jXft39+/ncaMeUW0tGnTRosXz9CoUdMVCCxp9PMDgSUa\nNWq6Fi+erkAgEIUKAQAAAACxiAYlgJhx0klSt27h2eHDzsjJSNq/X7rySqdJWdfo0c5OSsSeKVPM\nJtmePdLs2f7Uk4jmzpUqK8OzDh2kG27wp55Y4XaDkgYljiZW9k/WoEGJaAoEAlq06H7NmFGqDh3y\nJbnMNzYUqk+ffM2YUapFi+6nOQkAAAAASYYGJYCYYVnR30NZXS1973vSe++Zjw0YID35pJSaGrnX\nQ+T07SuNHWvmv/+9VFHhfT2JJhRyb/aOHy+1auV9PbHErUG5fr3zDRRAfWJl/2SNoUPNv98++cT5\nAUSCZVnKzR2rjh3vlbRR0s8l/UrS47KsxZIWS3pc0t1HHtuo5567V7m5YxnrCgAAAABJiAYlgJgS\n7Qbl3XdLCxaYeadO0osvSm3bRu61EHlTpkgpdf7mKilx35uIxlm4UNq+PTyzLCk31596YknXrs5N\n0toOHpS2bvWnHsSHWLtBmZEhnXmmmbOHEpG0aZO0ZUu6pBsl3SdpmqRz9I9/tFL//q0knSNp6pHH\nbtT69em+1QoAAAAA8BcNSgAxxa1BuWqVc/OxuR5/XPrNb8w8PV16/nnp5JOb/xqIrj59pHHjzHzG\nDKm83Pt6EklBgZldcYXUvbvnpcQcy3K/Rblunfe1ID6UlkqffhqepaS4/z7yEmNeEW3PPVc3SdHg\nwb119dVDNWzYUEm9Vft/gro18gEAAAAAyYEGJYCYcvrpUuvW4dnevc535DfHm286oyrd/OUv0qBB\nzTsf3rn7bvMW5ZdfujfY0DAbN0pLlpj5pEne1xKrsrLMjD2UqI/beNc+fcy/37w2bJiZLVsm2bbX\nlSBRmQ1K6ZprnP+bnW0+RoMSAAAAAJIXDUoAMSUtTTr3XDNvzpjXjz92PhyrqjIfmzpVuuGGpp8N\n7/XqJd10k5nPmCGVlXlfTyJ48EEz69VLGjHC+1pildvNNxqUqE+s7Z+sMXSo1KJFeLZ9u7Rtmz/1\nILF89pm0Zo2Z1zQo3f4MvP8+DXIAAAAASFY0KAHEnEjuofz6a2dM5Zdfmo/dcINzGw/x51e/klJT\nw7OvvpJmzvSnnnhWVubcIq5r0iTzpmoyo0GJxoi1/ZM12rSRzj7bzBnzikh4/nkz699f6tnTZxC+\nYQAAIABJREFU+XnfvlLLluGPf/GFtGtX9GsDAAAAAMQePnoEEHMi1aA8fFgaO1basMF87Nxzpcce\nc3bLIf707Cl973tmfv/9TlMaDff44+b+zkBAuvlmf+qJVX37mg3b7dudEdRAXbHaoJTc91AuW+Z5\nGUhAbg3KmtuTktOcHDDA/BrGvAIAAABAcqJBCSDmDB5sNg63bpX27GncOT/9qfTqq2Z+0knOh2it\nWjW9Rvjvf//XHFW4d6/0pz/5U088sm1p1iwz/973pHbtvK8nlrVuLWVmmjm3KFHXvn3Sli1mPnCg\n97W4cdtDuXQpYzbRPF995d7ort2glOof8woAAAAASD4NalBalnWpZVmbLMvaYlnWXS6PZ1iW9YJl\nWWstyyqyLOv7R/J0y7Letiyr8Eg+JcL1A0hA7do5I8HqWr264WfMmuU+7rNNG+nFF6XOnZteH2JD\njx7ut/z++EdutTXU0qXSxo1mPmmS97XEg6wsM6NBibrWrTOzU0+Nnab/eec5+55r+/xz5xuBgKZa\nuFAKhcKzk082G/PZ2eZz3Xa2AgAAAAAS31EblJZlpUgqkHSJpH6SxlmW1afOl02StN627YGShkv6\ng2VZLWzbrpQ03LbtbEkDJY2yLOuciP4TAEhIQ4aYWUPHvL72mnT77WZuWdJTT7nvkkN8crtF+fXX\n0v/9nz/1xJuCAjMbNkzq18/zUuICeyjRELE83lVybgOfe66Zs4cSzfHcc2Z29dXmRAxuUAIAAAAA\najTkBuU5kopt2/7Utu1Dkp6WdFWdr7EltT3y87aSSm3bPixJtm3vP5KnS2px5GsB4Fs1dQ/lhg3S\nmDHmd/FLzn7C0aObXxtiR/fu0i23mPn/+3/OuDnUb/t26Z//NPO8PO9riRc0KNEQsd6glNzHvLKH\nEk21f7/7SP26410l5300NTU8275d+vLL6NQGAAAAAIhdDWlQdpO0o9avPzuS1VYgqa9lWTslrZP0\nn7tLlmWlWJZVKGm3pMW2ba9pXskAkkF4gzIkabPWrHlTr7/+pjZv3qzq6mrjOSUlTgOyrMw8b8IE\n6Sc/iVa18NMvf2mOKywrc0a9on4PPSTV/WN0wgnSVXW/BQn/4dagLCoy/z0iubk1KN3GWvpp+HAz\nYw8lmurVV6UDB8Kzjh2loUPNr23VSjrtNDNnzCsAAAAAJJ8G7aBsgEs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zX6e5O5QAAAAA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\n84njOPdYXq/rOM4sx3FWOI6zynGcH5R67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z16zNettw6Ow1kIC8dhD2UQdu+WZs8247ZR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f272a977c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(32,20))\n",
    "plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\n",
    "#plt.axis([0, max(parameter_values), 0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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058MMRv5VH0AjBnFOy1Zm1nvfhpnh7tbNOsn9FsrAaWi9eAnDkEymRntsT6Vg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f272a9e5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for parameter, scores in zip(parameter_values, all_scores):\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f272a0a0978>,\n",
       " <matplotlib.lines.Line2D at 0x7f272a0a0c18>,\n",
       " <matplotlib.lines.Line2D at 0x7f272a0a0e10>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f272ad25400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, all_scores, 'bx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "all_scores = defaultdict(list)\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    for i in range(100):\n",
    "        estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "        scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10)\n",
    "        all_scores[n_neighbors].append(scores)\n",
    "for parameter in parameter_values:\n",
    "    scores = all_scores[parameter]\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(parameter_values, avg_scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
